Results

cesm2.ssp245

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means -0.431 -0.509 -0.528 -0.621
ratio_of_sd 0.964 0.997 0.947 0.926
amplitude_ratio_of_means 0.750 0.956 0.818 0.719
maximum_error 0.441 0.126 0.256 0.269
ks_mean_on_coarse_res_with_extremes 0.287 0.137 0.165 0.281
qqplot_mae 1.255 1.482 1.541 1.810
acf_mae 0.128 0.034 0.080 0.112
extremogram_mae 0.078 0.041 0.019 0.040

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler

cesm2.ssp370

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means -0.498 -0.567 -0.594 -0.677
ratio_of_sd 0.929 0.967 0.917 0.895
amplitude_ratio_of_means 0.741 0.959 0.809 0.708
maximum_error 0.425 0.127 0.247 0.273
ks_mean_on_coarse_res_with_extremes 0.209 0.041 0.118 0.224
qqplot_mae 1.450 1.651 1.735 1.970
acf_mae 0.119 0.019 0.072 0.105
extremogram_mae 0.042 0.017 0.017 0.021

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler

cesm2.ssp585

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means -0.370 -0.444 -0.472 -0.559
ratio_of_sd 0.958 0.995 0.947 0.923
amplitude_ratio_of_means 0.744 0.958 0.811 0.710
maximum_error 0.415 0.111 0.249 0.260
ks_mean_on_coarse_res_with_extremes 0.168 0.053 0.089 0.190
qqplot_mae 1.081 1.294 1.379 1.628
acf_mae 0.126 0.031 0.082 0.113
extremogram_mae 0.060 0.016 0.012 0.031

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler

ec_earth3.ssp434

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means -0.186 -0.266 -0.284 -0.392
ratio_of_sd 0.974 1.019 0.965 0.933
amplitude_ratio_of_means 0.668 0.946 0.718 0.619
maximum_error 0.454 0.163 0.259 0.336
ks_mean_on_coarse_res_with_extremes 0.370 0.176 0.285 0.373
qqplot_mae 0.562 0.775 0.838 1.141
acf_mae 0.174 0.065 0.139 0.167
extremogram_mae 0.071 0.021 0.032 0.052

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler

mri_esm2_0.ssp245

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means -0.152 -0.319 -0.337 -0.412
ratio_of_sd 1.141 1.153 1.094 1.090
amplitude_ratio_of_means 0.831 0.962 0.922 0.817
maximum_error 0.441 0.112 0.216 0.299
ks_mean_on_coarse_res_with_extremes 0.421 0.304 0.294 0.349
qqplot_mae 0.768 1.031 0.983 1.203
acf_mae 0.144 0.086 0.097 0.122
extremogram_mae 0.108 0.100 0.020 0.019

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler

mri_esm2_0.ssp370

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means 0.029 -0.134 -0.170 -0.241
ratio_of_sd 1.086 1.109 1.061 1.042
amplitude_ratio_of_means 0.789 0.961 0.884 0.769
maximum_error 0.428 0.114 0.224 0.325
ks_mean_on_coarse_res_with_extremes 0.433 0.287 0.299 0.401
qqplot_mae 0.546 0.631 0.541 0.703
acf_mae 0.139 0.068 0.096 0.122
extremogram_mae 0.072 0.034 0.025 0.037

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler

mri_esm2_0.ssp434

<<<<<<< HEAD
Metric cnn naive xgboost lstm
diff_of_means 0.000 -0.165 -0.198 -0.266
ratio_of_sd 1.141 1.155 1.099 1.091
amplitude_ratio_of_means 0.815 0.963 0.901 0.799
maximum_error 0.440 0.108 0.215 0.316
ks_mean_on_coarse_res_with_extremes 0.413 0.276 0.297 0.373
qqplot_mae 0.765 0.878 0.724 0.778
acf_mae 0.146 0.085 0.106 0.127
extremogram_mae 0.076 0.053 0.020 0.022

Time series of the first days

<<<<<<< HEAD

=======

>>>>>>> scaler

Plots

Important: Right now we are only estimating the upper tail extremogram. Currently we didn’t find a way to estimate the two tales at the same time. We are using quant = .97

<<<<<<< HEAD

=======

>>>>>>> scaler